Current Research

My research interests lie in the broad fields of hydroclimatology and climate impacts on coupled human-natural systems. More specifically, I investigate how hydrologic processes might change at regional scales under climate change, and how they might affect water resource systems that humans and landscapes depend on. Here I lay out my current research activities under three major thrusts:

Drought and climate change

Whether or not climate change will increase the aridity of the mid-latitude continental areas around the globe (including the central and western U.S.) later in the 21st century is a question still largely debated in the current scientific literature. My research on this topic has been centered on the role of evaporative demand (E0) as an indicator of future drought risk. Using projections from Global Climate Models (GCMs) with varying degrees of climate sensitivity from the Coupled Model Intercomparison Project 5 (CMIP5), my colleagues and I have shown that different methods used to estimate E0 from GCM output lead to a wide range of estimates of drought risk in the future (Dewes et al., 2017). Methods using only temperature to estimate E0, as opposed to those that fully represent the moist, radiative, and turbulent properties of the near-surface atmosphere, tend to enhance – and potentially overestimate – the drying trend throughout the 21st century. On the other hand, even the fully physical methods of estimating E0 are likely not appropriate to estimate future drought risk because they don’t account for adaptive mechanisms of plants to higher atmospheric carbon dioxide concentrations, such as increased water-use efficiency and leaf area, which in turn would affect the actual evapotranspiration.

My current research aims to better understand these shortcomings, and I have broadened my focus to pursue a more complete assessment of the land surface water balance. I am now investigating how soil moisture (SM) and actual evapotranspiration (aET) respond to variations in E0. To this end, I make use of output from land surface models that are driven by observational data. A particular case study of interest is the 2017 drought event in the Upper Missouri basin region, which had devastating impacts on agriculture and ranching and also caused extensive wildfires. In a recently funded project, I will be analyzing the various components of the water and energy balances throughout the evolution of this event to identify the drivers of its onset, intensification, and demise. The project team will also collectively be investigating whether climate change had an impact on the rapid onset and intensity of this event, and whether a better understanding of the physical driving processes could lead to improved predictability of droughts in general.

Climate impacts on coupled natural-human systems

Another aspect of my research on drought involves understanding the impacts of hydroclimatic variability and change on coupled natural-human systems. My focus here has been on the Wind River area in Wyoming, where I investigate the role of precipitation over mountains falling as rain versus snow. The main hypothesis is that with the anthropogenic warming of the atmosphere, freezing levels are increasingly higher, leaving the land surface more likely to receive precipitation as rain. This implies that, instead of accumulating a snowpack that would slowly melt away in spring and make water available to downstream communities throughout the semi-arid summer, this snowpack is now much smaller and quicker to deteriorate. As a result, stream flow peaks much earlier, and if spring temperatures are significantly high, the volume of flow might overwhelm the channels and reservoirs below, forcing water managers to release the water to continue its course. With a smaller snowpack on the mountain, not much is left to flow during the summer after the rapid spring melt flow has passed. The Wind River Indian Reservation (WRIR) experienced this scenario during the water-year 2015, which was documented by my colleagues and I in a recently published case-study (McNeeley et al., 2017). In this study, I evaluated the hydroclimatic conditions during the winter, spring, and summer of 2015, and found that precipitation had actually reached around 120% of normal over the region. Snowfall, on the other hand, was significantly below normal, especially at higher elevations, meaning the surplus in precipitation came down as rain. Winter and spring were abnormally warm in the region, even more so over the mountains. Reservoirs in the WRIR did not have the capacity to hold back the anomalously high spring runoff, which was augmented with record-breaking rainfall in May. These were followed by a hot and dry summer; farmers and ranchers were hit with a rapidly drying landscape, and the irrigation season was terminated about six weeks earlier than normal because reservoirs were dry.

I am also investigating whether this particular water-year was a product of the interannual variability of the hydroclimate of the Wind River area, or if a climate change signal is detectable. Making use of a large and diverse array of station, gridded, and remotely sensed data sets – albeit no longer than 30 years of records –, I have found that the interannual variability in snowfall in this region is large enough to overshadow any long-term trends. I did find, however, strong correlations between the elevation of early spring snowline, the date of peak SWE (snow water equivalent), and the date of peak stream flow and the springtime average freezing level. This indicates that the large-scale atmospheric conditions are indeed controlling the quality, depth, and duration of the mountain snowpack, and that while a climate change signal is not quite evident in the observations, yet, we have significant confidence that continued warming will take place and the impacts will become more obvious over time. A manuscript on this work is in preparation and will be submitted for publication in the coming months (Dewes et al, in preparation).

Use of climate projections in applied sciences

The third thrust of my research relates to making the best-available climate change information accessible to other applied sciences, many of which need it to produce assessments of vulnerability and adaptation for their specific systems of interest. This becomes a mix of collaborative research (or “co-production”) and outreach (or “climate services”), where my research is guided by the needs of users of climate information and in turn I engage with them to provide expert guidance on the values and caveats of the many existing datasets that are publicly available. I will here focus on the research aspect of this thrust.

A major commonality among users of climate change information is the need for spatial resolution that is higher than those attainable with GCMs (currently on the order of 1-3 degrees latitude by longitude). While Regional Climate Modeling efforts are being made to dynamically downscale climate projections to resolutions on the order of tens of kilometers, these experiments tend to be very computationally expensive, regionally limited (by definition), and quite often do not realistically represent the full range of uncertainty. Furthermore, they still don’t reach the fine spatial resolution desired by the climate impacts community (on the order of hundreds of meters). Statistical downscaling (SD) has been filling that gap, by aiming to add value to GCM projections by reducing model biases and adding finer spatial detail. Several products derived using different methods have been made available in the past decade. Statistical computations tend to be much faster than the solution of physical equations, which lowers computational costs and allows for the consideration of a larger number of scenarios (i.e., parent GCMs), thus improving the representation of uncertainty. The flipside is that each SD method comes with its own set of assumptions, the most common one being the assumption that relationships observed in the historical climate will hold in the future (i.e., that climate is stationary).

I recently joined a project that aims to understand if, where, and how this assumption is violated. Because we do not have observational data for the future, we make use of a “perfect model” approach, in which the data from a Regional Climate Model (RCM) is our “truth” for both historical and future climates. These RCM output are coarsened to GCM-scale resolutions (~200 km), and then downscaled again using several different SD techniques. In this capacity I will be responsible for the comparison between the downscaled products and the RCM “truth”, to assess what was the added-value of the SD method, and what were the implications of assuming stationarity in climate. A better understanding of these outcomes will allow us to communicate the advantages and shortcomings of certain popular SD methods, which will enable users of climate change data to appropriately acknowledge the strengths and limitations of their assessments.

A different approach to evaluate an existing set of statistically downscaled projections derived from my research on evaporative demand (E0) and future drought risk. I learned that E0 is a very important variable in ecological and species distribution modeling, and that ecologists have a strong and legitimate need for data at very high resolutions. It turns out that the Multivariate Adaptive Constructed Analogs (MACA) dataset, which is a collection of statistically downscaled projections from CMIP5 models, includes all the variables needed to properly estimate E0 (as I presented above). This makes MACA a very attractive product among ecological modelers, but it does not mean they have the ability to ingest and compute E0 at scales large enough to visualize the spatio-temporal patterns of the data and decide if they are climatically meaningful. To that end, I carried out a comparison between E0 from five different models, in both the MACA and the native resolutions. I found that the bias-correction techniques employed by MACA effectively adjust the modeled historical climate to match the observations, yet without losing the native GCMs’ patterns of interannual variability. This is important, as this variability is what allows us to estimate the range of uncertainty in climate changes going into the future. Second, the patterns and magnitude of change in mean E0 by mid-21st century, from the native GCMs, are not lost in the SD process. Third, shifts in the distributions of weekly and seasonal E0 projected for the 2050s are very similar between MACA and the native GCMs, meaning the representation of extreme events is also not compromised with the SD process. A manuscript describing the findings of this work is currently in preparation and will be submitted in the coming months (Dewes et al, in preparation).